A Mono Surrogate for Multiobjective Optimization

Ilya Loshchilov 1 Marc Schoenauer 1, 2 Michèle Sebag 1, 2
1 TAO - Machine Learning and Optimisation
LRI - Laboratoire de Recherche en Informatique, UP11 - Université Paris-Sud - Paris 11, Inria Saclay - Ile de France, CNRS - Centre National de la Recherche Scientifique : UMR8623
Abstract : Most surrogate approaches to multi-objective optimization build a surrogate model for each objective. These surrogates can be used inside a classical Evolutionary Multiobjective Optimization Algorithm (EMOA) in lieu of the actual objectives, without modifying the underlying EMOA; or to filter out points that the models predict to be uninteresting. In contrast, the proposed approach aims at building a global surrogate model defined on the decision space and tightly characterizing the current Pareto set and the dominated region, in order to speed up the evolution progress toward the true Pareto set. This surrogate model is specified by combining a One-class Support Vector Machine (SVMs) to characterize the dominated points, and a Regression SVM to clamp the Pareto front on a single value. The resulting surrogate model is then used within state-of-the-art EMOAs to pre-screen the individuals generated by application of standard variation operators. Empirical validation on classical MOO benchmark problems shows a significant reduction of the number of evaluations of the actual objective functions.
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Conference papers
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https://hal.inria.fr/inria-00483948
Contributor : Loshchilov Ilya <>
Submitted on : Monday, May 17, 2010 - 3:22:53 PM
Last modification on : Thursday, April 5, 2018 - 12:30:12 PM
Long-term archiving on : Thursday, September 16, 2010 - 2:55:44 PM

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Ilya Loshchilov, Marc Schoenauer, Michèle Sebag. A Mono Surrogate for Multiobjective Optimization. Genetic and Evolutionary Computation Conference 2010 (GECCO-2010), Jul 2010, Portland, OR, United States. ⟨inria-00483948⟩

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